We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.

中文翻译：

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图像投影网络：OCTA图像中的3D到2D图像分割。

我们提出了一种图像投影网络（IPN），它是一种新颖的端到端架构，可以在光学相干断层扫描血管造影（OCTA）图像中实现3D到2D图像分割。我们的主要见识在于构建投影学习模块（PLM），该模块使用单向池层同时进行有效的特征选择和尺寸缩减。通过组合多个PLM，建议的网络可以输入3D OCTA数据，并输出2D分割结果，例如视网膜血管分割。它为量化视网膜指标提供了新思路：无需视网膜层分割和投影图。我们针对两个关键的视网膜图像分割问题测试了我们的网络性能：视网膜血管（RV）分割和小凹性无血管区域（FAZ）分割。